import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
import torchvision

from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from mmseg.utils import get_root_logger
from mmcv.runner import load_checkpoint
from .resnet_cbam import BasicBlock


model_path = {
    'resnet18': 'pretrained/resnet18.pth',
    'resnet34': 'pretrained/resnet34.pth'
}


def get_resnet18(pretrained=True):
    net = torchvision.models.resnet18(pretrained=False)
    if pretrained:
        state_dict = torch.load(model_path['resnet18'])
        net.load_state_dict(state_dict)

    return net


def get_resnet34(pretrained=True):
    net = torchvision.models.resnet34(pretrained=False)
    if pretrained:
        state_dict = torch.load(model_path['resnet34'])
        net.load_state_dict(state_dict)

    return net



class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


# 代理注意力机制 wkl         
class AgentAttention(nn.Module):
    def __init__(self, dim, num_patches, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,
                 sr_ratio=1, agent_num=49, **kwargs):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
        self.dim = dim
        # dim 64 128 320
        self.num_patches = num_patches
        window_size = (int(num_patches ** 0.5), int(num_patches ** 0.5))
        self.window_size = window_size
        # print(f"windows size{window_size}")
        # windows size(112, 112)
        # windows size(28, 28)
        # windows size(14, 14)
        # windows size(7, 7)
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
            self.norm = nn.LayerNorm(dim)

        self.agent_num = agent_num
        self.dwc = nn.Conv2d(in_channels=dim, out_channels=dim, kernel_size=(3, 3), padding=1, groups=dim)
        self.an_bias = nn.Parameter(torch.zeros(num_heads, agent_num, 7, 7))
        self.na_bias = nn.Parameter(torch.zeros(num_heads, agent_num, 7, 7))
        self.ah_bias = nn.Parameter(torch.zeros(1, num_heads, agent_num, window_size[0] // sr_ratio, 1))
        self.aw_bias = nn.Parameter(torch.zeros(1, num_heads, agent_num, 1, window_size[1] // sr_ratio))
        self.ha_bias = nn.Parameter(torch.zeros(1, num_heads, window_size[0], 1, agent_num))
        self.wa_bias = nn.Parameter(torch.zeros(1, num_heads, 1, window_size[1], agent_num))
        trunc_normal_(self.an_bias, std=.02)
        trunc_normal_(self.na_bias, std=.02)
        trunc_normal_(self.ah_bias, std=.02)
        trunc_normal_(self.aw_bias, std=.02)
        trunc_normal_(self.ha_bias, std=.02)
        trunc_normal_(self.wa_bias, std=.02)
        pool_size = int(agent_num ** 0.5)
        self.pool = nn.AdaptiveAvgPool2d(output_size=(pool_size, pool_size))
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, H, W):
        b, n, c = x.shape
        # 序列长度n 的大小为18240
        # b,n,c 自注意尺度为
        # (3, 18240, 64)  8
        # (3, 4560, 128) sr_ratio的大小为 4
        # (3, 1140, 320)   2
        # (3, 266, 512)    1
        print(f"序列长度n 的大小为{n}")
        num_heads = self.num_heads
        head_dim = c // num_heads
        q = self.q(x)

        if self.sr_ratio > 1:
            x_ = x.permute(0, 2, 1).reshape(b, c, H, W)  #x.shape=[3, 18240, 64]    x_.shape=[3, 64, 60, 304]
            x_ = self.sr(x_).reshape(b, c, -1).permute(0, 2, 1)
            x_ = self.norm(x_)         # x_.shape= ([3, 266, 64])
            kv = self.kv(x_).reshape(b, -1, 2, c).permute(2, 0, 1, 3)
            # print(f"xxx{kv.shape}")
            # kv.shape形状torch.Size([2, 3, 266, 64])  第二个维度 b 是批次大小。266自动计算的维度大小,第四个维度 64 是通道数或特征的数量。
        else:
            kv = self.kv(x).reshape(b, -1, 2, c).permute(2, 0, 1, 3)

        k, v = kv[0], kv[1]
        # 118line-----k.shape为:torch.Size([3, 266, 64])
        print(f"126line-----k.shape为:{k.shape},h和w分别为:{H},{W}")  #h 为60，w为304
        agent_tokens = self.pool(q.reshape(b, H, W, c).permute(0, 3, 1, 2)).reshape(b, c, -1).permute(0, 2, 1)
        # 假设 x 的形状是 (b, n, c)，H 和 W 是输入的高和宽
        assert n == H * W, "n 应该等于 H * W"

        q = q.reshape(b, n, num_heads, head_dim).permute(0, 2, 1, 3)
        k = k.reshape(b, n // self.sr_ratio ** 2, num_heads, head_dim).permute(0, 2, 1, 3)
        v = v.reshape(b, n // self.sr_ratio ** 2, num_heads, head_dim).permute(0, 2, 1, 3)
        agent_tokens = agent_tokens.reshape(b, self.agent_num, num_heads, head_dim).permute(0, 2, 1, 3)
        print(f'采样率比率self.sr_ratio和n:{self.sr_ratio,n}')    # 8
###############################################################################################################3

###############################################################################################################3

        kv_size = (self.window_size[0] // self.sr_ratio, self.window_size[1] // self.sr_ratio)
        position_bias1 = nn.functional.interpolate(self.an_bias, size=kv_size, mode='bilinear')
        position_bias1 = position_bias1.reshape(1, num_heads, self.agent_num, -1).repeat(b, 1, 1, 1)
        position_bias2 = (self.ah_bias + self.aw_bias).reshape(1, num_heads, self.agent_num, -1).repeat(b, 1, 1, 1)
        position_bias = position_bias1 + position_bias2
        print(f"175linekkkkkkkk{k.shape}")
        agent_attn = self.softmax((agent_tokens * self.scale) @ k.transpose(-2, -1) + position_bias)
        agent_attn = self.attn_drop(agent_attn)
        agent_v = agent_attn @ v

        agent_bias1 = nn.functional.interpolate(self.na_bias, size=self.window_size, mode='bilinear')
        agent_bias1 = agent_bias1.reshape(1, num_heads, self.agent_num, -1).permute(0, 1, 3, 2).repeat(b, 1, 1, 1)
        agent_bias2 = (self.ha_bias + self.wa_bias).reshape(1, num_heads, -1, self.agent_num).repeat(b, 1, 1, 1)
        agent_bias = agent_bias1 + agent_bias2
        q_attn = self.softmax((q * self.scale) @ agent_tokens.transpose(-2, -1) + agent_bias)
        q_attn = self.attn_drop(q_attn)
        x = q_attn @ agent_v

        x = x.transpose(1, 2).reshape(b, n, c)
        v = v.transpose(1, 2).reshape(b, H // self.sr_ratio, W // self.sr_ratio, c).permute(0, 3, 1, 2)
        if self.sr_ratio > 1:
            v = nn.functional.interpolate(v, size=(H, W), mode='bilinear')
        x = x + self.dwc(v).permute(0, 2, 3, 1).reshape(b, n, c)

        x = self.proj(x)
        x = self.proj_drop(x)
        return x
    
# 多头注意力
class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
            self.norm = nn.LayerNorm(dim)

    def forward(self, x, H, W):
        B, N, C = x.shape
        # b,n,c 自注意尺度为
        # (3, 18240, 64)  8
        # (3, 4560, 128) sr_ratio的大小为 4
        # (3, 1140, 320)   2
        # (3, 266, 512)    1
        # print(f"b,n,c 自注意尺度为{B,N,C}")
        # print(f"self.sr_ratio的大小为{self.sr_ratio}")
        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

        if self.sr_ratio > 1:
            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
            x_ = self.norm(x_)
            kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        else:
            kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class Block(nn.Module):

    def __init__(self, dim, num_heads,num_patches, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1,agent_num=49,attn_type='A'):
        super().__init__()
        self.norm1 = norm_layer(dim)
        if attn_type == 'A':
            self.attn = AgentAttention(
                    dim, num_patches,
                    num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
                    attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio,
                    agent_num=agent_num)
        else:
            self.attn = Attention(
                dim,
                num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
                attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)

        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        self.resblock = BasicBlock(dim, dim, ratio=16)
        self.concat_conv = nn.Conv2d(dim*2, dim, kernel_size=(3, 3), padding=(1, 1), bias=False)

    def forward(self, x, H, W):
        input = x

        # Transformer branch
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        # CNN branch
        B, N, C = input.shape
        _, _, Cx = x.shape
        input = input.transpose(1, 2).view(B, C, H, W)
        input = self.resblock(input)

        # fusion
        x = x.transpose(1, 2).view(B, Cx, H, W)
        x = self.concat_conv(torch.cat([x, input], dim=1))
        x = x.flatten(2).transpose(1, 2)

        return x


class PatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """

    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)

        self.img_size = img_size
        self.patch_size = patch_size
        assert img_size[0] % patch_size[0] == 0 and img_size[1] % patch_size[1] == 0, \
            f"img_size {img_size} should be divided by patch_size {patch_size}."
        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
        self.num_patches = self.H * self.W
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = nn.LayerNorm(embed_dim)

    def forward(self, x):
        B, C, H, W = x.shape

        x = self.proj(x).flatten(2).transpose(1, 2)
        x = self.norm(x)
        H, W = H // self.patch_size[0], W // self.patch_size[1]

        return x, (H, W)


class PyramidVisionTransformer(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, depths=[3, 4, 6, 3],
                 sr_ratios=[8, 4, 2, 1], num_stages=4, pretrained=None):
        super().__init__()
        self.depths = depths
        self.num_stages = num_stages
        net = get_resnet34(pretrained=True)
        setattr(self, "embed_layer1", net.layer1)
        setattr(self, "embed_layer2", net.layer2)
        del net
        in_chans = 128
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        cur = 0

        for i in range(num_stages):
            patch_embed = PatchEmbed(img_size=img_size if i == 0 else img_size // (2 ** (i + 1)),
                                     patch_size= patch_size if i == 0 else 2,
                                     in_chans=in_chans if i == 0 else embed_dims[i - 1],
                                     embed_dim=embed_dims[i])
            num_patches = patch_embed.num_patches if i != num_stages - 1 else patch_embed.num_patches + 1
            pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dims[i]))
            pos_drop = nn.Dropout(p=drop_rate)
###########################################################wkl
            block = nn.ModuleList([Block(
                dim=embed_dims[i],num_heads=num_heads[i],num_patches=num_patches,mlp_ratio=mlp_ratios[i], qkv_bias=qkv_bias,
                qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + j],
                norm_layer=norm_layer, sr_ratio=sr_ratios[i])
                for j in range(depths[i])])
            cur += depths[i]

            setattr(self, f"patch_embed{i + 1}", patch_embed)
            setattr(self, f"pos_embed{i + 1}", pos_embed)
            setattr(self, f"pos_drop{i + 1}", pos_drop)
            setattr(self, f"block{i + 1}", block)

            trunc_normal_(pos_embed, std=.02)

        # init weights
        self.apply(self._init_weights)
        self.init_weights(pretrained)

    def init_weights(self, pretrained=None):
        if isinstance(pretrained, str):
            logger = get_root_logger()
            load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
            print("===pretrained weight loaded===")

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def _get_pos_embed(self, pos_embed, patch_embed, H, W):
        if H * W == self.patch_embed1.num_patches:
            return pos_embed
        else:
            return F.interpolate(
                pos_embed.reshape(1, patch_embed.H, patch_embed.W, -1).permute(0, 3, 1, 2),
                size=(H, W), mode="bilinear").reshape(1, -1, H * W).permute(0, 2, 1)

    def forward_features(self, x):
        outs = []
        B = x.shape[0]
        x = getattr(self, 'embed_layer1')(x)
        outs.append(x)
        x = getattr(self, 'embed_layer2')(x)
        outs.append(x)


        for i in range(self.num_stages):
            patch_embed = getattr(self, f"patch_embed{i + 1}")
            pos_embed = getattr(self, f"pos_embed{i + 1}")
            pos_drop = getattr(self, f"pos_drop{i + 1}")
            block = getattr(self, f"block{i + 1}")
            x, (H, W) = patch_embed(x)
            if i == self.num_stages - 1:
                pos_embed = self._get_pos_embed(pos_embed[:, 1:], patch_embed, H, W)
            else:
                pos_embed = self._get_pos_embed(pos_embed, patch_embed, H, W)

            x = pos_drop(x + pos_embed)
            for blk in block:
                x = blk(x, H, W)
            x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
            outs.append(x)

        return outs

    def forward(self, x):
        x = self.forward_features(x)

        return x


def _conv_filter(state_dict, patch_size=16):
    """ convert patch embedding weight from manual patchify + linear proj to conv"""
    out_dict = {}
    for k, v in state_dict.items():
        if 'patch_embed.proj.weight' in k:
            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
        out_dict[k] = v

    return out_dict


class PVT(PyramidVisionTransformer):
    def __init__(self, in_chans, patch_size=4, **kwargs):
        super(PVT, self).__init__(
            patch_size=patch_size, in_chans=in_chans, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3],
            sr_ratios=[8, 4, 2, 1], drop_rate=0.0, drop_path_rate=0.1, pretrained=kwargs['pretrained'])


